Method for modeling behavior and psychotic disorders

ABSTRACT

A method and system for modeling behavior and a psychotic disorder-related state of a patient, the method comprising: receiving a log of use dataset associated with communication behavior of the patient during a time period; receiving a supplementary dataset characterizing mobility-behavior of the patient during the time period; generating a predictive model based upon a passive dataset derived from the log of use dataset and the supplementary dataset; transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the individual associated with at least a portion of the time period; and upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for the individual by way of at least one of the computing system and the mobile communication device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S.application Ser. No. 13/969,339 filed 16 Aug. 2013, which claims thebenefit of U.S. Provisional Application Ser. No. 61/683,867 filed on 16Aug. 2012 and U.S. Provisional Application Ser. No. 61/683,869 filed on16 Aug. 2012, which are each incorporated in its entirety herein by thisreference.

This application also claims the benefit of U.S. Provisional ApplicationSer. No. 62/043,248 filed 28 Aug. 2014, and U.S. Provisional ApplicationSer. No. 62/085,726 filed 1 Dec. 2014, which are each incorporated inits entirety herein by this reference.

TECHNICAL FIELD

This invention relates generally to the field of patient health and morespecifically to a new and useful method for modeling behavior andpsychosis in the field of patient health.

BACKGROUND

Schizophrenia spectrum and psychotic disorders (as described, forinstance, in editions of the Diagnostic and Statistical Manual of MentalDisorders) are characterized by a combination of symptoms that interferewith a person's ability to interact with society, work effectively,sleep, and maintain physical health. Psychosis is characterized by aloss of connection with reality (e.g., including hallucinations anddelusions). Early intervention in psychosis is crucial to affectingpatient outcomes, due to the rapid progression from stable to instablestates. However, early intervention requires intensive patientassessment and monitoring. Current systems and methods for monitoringpatients exhibiting symptoms of psychosis can influence patientoutcomes, but are typically time and/or cost-intensive or entirely failto identify when a patient is entering a critical state of psychosis atwhich intervention would be most effective. As such, current standardsof detection, diagnosis and treatment of psychotic disorders, as well asbarriers (e.g., social barriers) to seeking diagnosis and treatment, areresponsible for delays in diagnoses of disorders and/or misdiagnoses ofdisorders, which cause psychotic disorders to remain untreated.Furthermore, such standards result in a reactionary approach, as opposedto a preventative approach to a psychosis-related event. Even further,changes in psychotic state can go undetected, resulting in regressionsin psychotic state, patient harm (e.g., self-inflicted or outwardlyafflicted), or even death. While the delays can be due to the sensitivenature of such disorders, current standards of detection diagnosis areseverely deficient in many controllable aspects. In addition to thesedeficiencies, further limitations in detection, diagnosis, treatment,and/or monitoring of patient progress during treatment prevent adequatecare of patients with diagnosable and treatable psychotic disorders.

As such, there is a need in the field of patient health for a new anduseful method for modeling behavior and psychosis. This inventioncreates such a new and useful method for modeling behavior andpsychosis.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart of an embodiment of a method for modeling behaviorand psychosis;

FIG. 2 depicts a flowchart of a portion of an embodiment of a method formodeling behavior and psychosis;

FIG. 3 depicts a flowchart of a portion of an embodiment of a method formodeling behavior and psychosis;

FIG. 4 depicts an example of a dashboard for providing an alert in anembodiment of a method for modeling behavior and psychosis;

FIGS. 5A-5C depict example notifications in an example of a method formodeling behavior and psychosis;

FIG. 6 depicts an embodiment of a system for modeling behavior andpsychosis.

DESCRIPTION OF THE EMBODIMENTS

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Method

As shown in FIG. 1, a method 100 for modeling behavior and psychosis ofa patient includes: receiving a log of use dataset of a communicationmodule executing on a mobile computing device by the patient during atime period S110; receiving a supplementary dataset characterizingmobility-behavior of the patient in association with the time periodS120; receiving a survey dataset including responses, to at least one ofa set of psychosis-assessment surveys, associated with a set of timepoints of the time period, from the patient S125; transforming dataderived from at least one the log of use dataset, the supplementarydataset, and the survey dataset into an analysis of a psychoticepisode-risk state associated with at least a portion of the time periodS130; and generating an alert based upon one or more outputs of theanalysis S150. In some variations, the method 100 can further includeany one or more of: providing a notification to the patient, at themobile communication device, in response to the analysis S160; andautomatically initiating provision of a therapeutic intervention for theindividual by way of at least one of the computing system and the mobilecommunication device S170.

The method 100 functions to analyze communication behavior and otherinformation regarding a patient (e.g., user, at-risk individual)exhibiting symptoms of psychosis, in order to assess risk of the patientin entering an adverse psychotic state (e.g., hallucinations,delusions). As such, the method 100 can facilitate monitoring of statesof psychosis in a patient exhibiting symptoms of psychosis, by enablingdetection of changes in the patient's condition. In a specificapplication, the method 100 can monitor and analyze communicationbehavior, mobility behavior, and/or other behavior detected from anyother suitable sensor(s) associated with a patient with a psychoticdisorder over time, and provide an alert to a caretaker associated withthe patient and/or to the patient upon detection that the patient hasentered or is at risk of entering a critical state of psychosis (e.g.,self-harming or outwardly-harming state). Thus, the method 100 canprovide a predictive model for one or more patients experiencingsymptoms of a psychotic disorder, as well as an intervention model forproviding interventions at key time points, to optimize improvement inpatient outcomes (e.g., as exhibited by an improved state). Theintervention model can thus implement an anticipated patient psychoticstate to drive automated or manual targeted intervention for a patient(e.g., via a phone call, email, health tip notification, insight, otherelectronic device-based messaging, other electronic device-basednotifications, or other electronic communication, etc.) in someapplications. In further embodiments, an analysis of the method 100 canbe used to generate and/or provide therapeutic regimens to the patientas a therapeutic measure in promoting the psychological health of thepatient.

As such, the method 100 can be used to monitor and/or treat patients atrisk for entering states associated with one or more schizophreniaspectrum and psychotic disorders (as described, for instance, ineditions of the Diagnostic and Statistical Manual of Mental Disorders),especially for patients diagnosed with or exhibiting symptoms of any oneor more of: schizophrenia, schizophreniform disorder, schizoaffectivedisorder, early psychosis, delusional disorder, brief psychoticdisorder, catatonia, attenuated psychosis syndrome, shared psychoticdisorder, psychotic disorder, substance-induced psychotic disorder, mooddisorders, bipolar disorders (e.g., Bipolar I disorder, Bipolar IIdisorder), cyclothymic disorder, premenstrual dysphoric disorder, andany other suitable psychotic disorder. Such symptoms can includedelusions, hallucinations, disorganized speech, disorganized behavior,catatonic behavior, reality distortion behavior, suicidal intent, andany other negative symptoms. In relation to psychosis and/or psychoticdisorder states described below, the variations of the method 100 canthus be used to detect and improve states associated with one or more ofthe above schizophrenia spectrum and psychotic disorders (as described,for instance, in editions of the Diagnostic and Statistical Manual ofMental Disorders), and/or any other suitable schizophrenia spectrum andpsychotic disorder.

While the method 100 can be implemented for a single patient exhibitingsymptoms of psychosis, the method 100 can additionally or alternativelybe implemented for a population of patients (e.g., including the entity,excluding the entity), wherein the population of patients can includepatients similar to and/or dissimilar to the patient (e.g., inexhibition of symptoms of psychosis, in demographic group, in medicalcondition, etc.). Thus, information derived from the population ofpatients can be used to provide additional insight into connectionsbetween the patient's behavior and risk of entering one of a spectrum ofpsychotic states, due to aggregation of data from a patient population.In a specific example, the method 100 involves a population of patientsbetween 14 and 30 years of age, each patient having a mobile computingdevice (e.g., smart phone, tablet, wearable computing device, etc.) andsuffering from a type of psychotic disorder (e.g., a disorder along aschizophrenia spectrum, a mood disorder with psychotic features). In thespecific example, the population of patients omits patients sufferingfrom visual or hearing impairment; however, variations of the specificexample can alternatively include patients of any other suitabledemographic or condition.

The method 100 is preferably implemented at least in part by anembodiment of the system 200 described in Section 2 below, variations ofwhich can be implemented at least in part by embodiments, variations,and examples of the system described in U.S. application Ser. No.13/969,339 entitled “Method for Modeling Behavior and Health Changes”and filed on 16 Aug. 2013; however, the method 100 can alternatively beimplemented using any other suitable system configured to processcommunication and/or other behavior of the patient, in aggregation withother information, in order to generate a model of behavior and state ofpsychosis in the patient.

1.1 Method—Passive Data

Block S110 recites: receiving a log of use dataset of a communicationmodule executing on a mobile computing device by the patient during atime period, which functions to unobtrusively collect and/or retrievecommunication-related data from a patient's mobile communication device.Preferably, Block S110 is implemented using a module of a computingsystem configured to interface with a native data collection applicationexecuting on a mobile communication device (e.g., smartphone, tablet,personal data assistant (PDA), personal music player, vehicle,head-mounted wearable computing device, wrist-mounted wearable computingdevice, etc.) of the patient, in order to retrieve patient communicationdata. As such, in one variation, a native data collection applicationcan be installed on the mobile communication device of the patient, canexecute substantially continuously while the mobile communication deviceis in an active state (e.g., in use, in an on-state, in a sleep state,etc.), and can record communication parameters (e.g., communicationtimes, durations, contact entities, etc.) of each inbound and/oroutbound communication (e.g., call, message) from the mobilecommunication device. In implementing Block S110, the mobilecommunication device can then upload this data to a database (e.g.,remote server, cloud computing system, storage module), at a desiredfrequency (e.g., in near real-time, every hour, at the end of each day,etc.) to be accessed by the computing system. In one example of BlockS110, the data collection application can launch on the patient's mobilecommunication device as a background process that gathers patient dataonce the patient logs into an account, wherein the patient data includeshow and with what frequency the patient interacts with and communicateswith other individuals through phone calls, e-mail, instant messaging,an online social network, and any other suitable mode of communication.

As such, in accessing the log of use of the native communicationapplication and receiving the log of use dataset, Block S110 preferablyenables collection of one or more of: phone call-related data (e.g.,number of sent and/or received calls, call duration, call start and/orend time, location of patient before, during, and/or after a call, andnumber of and time points of missed or ignored calls); text messaging(e.g., SMS test messaging) data (e.g., number of messages sent and/orreceived, message length associated with a contact of the individual,message entry speed, delay between message completion time point andsending time point, message efficiency, message accuracy, time of sentand/or received messages, location of the patient when receiving and/orsending a message); data on textual messages sent through othercommunication venues (e.g., public and/or private textual messages sentto contacts of the patient through an online social networking system,reviews of products, services, or businesses through an online rankingand/or review service, status updates, “likes” of content providedthrough an online social networking system), vocal and textual content(e.g., text and/or voice data that can be used to derive featuresindicative of negative or positive sentiments) and any other suitabletype of data.

In relation to receiving the log of use dataset, Block S110 can includeaccessing the log of use at the mobile communication device of theindividual, and transmitting, from the mobile communication device to acomputing system, a log of use dataset associated with communicationbehavior of the individual S112, as shown in FIG. 2. The computingsystem can be implemented in one or more of a processing module of themobile communication device, a personal computer, a remote server, acloud-based computing system, a computing module of any other suitablecomputing device (e.g., mobile computing device, wearable computingdevice, etc.), and any other suitable computing module. In transmittingthe log of use dataset, a communication module (e.g., a hardwarecommunication module associated with the communication application) cantransmit data to the computing system by way of a wired and/or wirelessdata link (e.g., over Bluetooth, over Bluetooth LTE, etc.). However,Block S110 can include another other suitable variation of accessing thelog of communication, transmitting data from the log of communication,and/or receiving a log of use dataset.

Block S120 recites: receiving a supplementary dataset characterizingmobility-behavior of the patient in association with the time period,which functions to unobtrusively receive non-communication-related datafrom a patient's mobile communication device and/or other deviceconfigured to receive contextual data from the patient. Block S120 caninclude receiving non-communication-related data pertaining to thepatient before, during, and/or after (or in the absence of)communication with another individual (e.g., a phone call) and/orcomputer network (e.g., a social networking application), as describedabove in relation to Block S110. Block S120 can include receiving one ormore of: location information, movement information (e.g., related tophysical isolation, related to lethargy), device usage information(e.g., screen usage information related to disturbed sleep,restlessness, and/or interest in mobile device activities), and anyother suitable information. In variations, Block S120 can includereceiving location information of the patient by way of one or more of:receiving a GPS location of the individual (e.g., from a GPS sensorwithin the mobile communication device of the patient), estimating thelocation of the patient through triangulation (e.g., triangulation oflocal cellular towers in communication with the mobile communicationdevice), identifying a geo-located local Wi-Fi hotspot during a phonecall, and in any other suitable manner. In applications, data receivedin Block S110 and S120 can be processed to track behaviorcharacteristics of the patient, such as mobility, periods of isolation,quality of life (e.g., work-life balance based on time spent at specificlocations), and any other location-derived behavior information.

As such, data from Blocks S110 and S120 can thus be processed separatelyand/or can be merged in subsequent blocks of the method 100 to track thepatient's mobility during a communication, for instance, in the analysisof Block S140. In variations, Block S120 can additionally oralternatively include receiving mobile usage data, including dataindicative of screen unlocks and mobile application usage (e.g., byretrieving usage information from mobile operating system logs, byretrieving usage information from a task manager on a mobilecommunication device, etc.). Blocks S120 and/or S110 can thereforefacilitate tracking of variations and periods of activity/inactivity fora patient through automatically collected data (e.g., from the patient'smobile communication device), in order to enable identification ofperiods of activity and inactivity by the patient (e.g., extendedperiods when the individual was hyperactive on the device or notasleep).

In additional variations, Block S120 can additionally or alternativelyinclude receiving one or more of: physical activity- or physicalaction-related data (e.g., accelerometer data, gyroscope data, data froman M7 or M8 chip) of the patient, local environmental data (e.g.,climate data, temperature data, light parameter data, etc.), nutritionor diet-related data (e.g., data from food establishment check-ins, datafrom spectrophotometric analysis, etc.) of the patient, biometric data(e.g., data recorded through sensors within the patient's mobilecommunication device, data recorded through a wearable or otherperipheral device in communication with the patient's mobilecommunication device) of the patient, and any other suitable data. Inexamples, one or more of: a blood pressure sensor, and a pulse-oximetersensor, and an activity tracker can transmit the individual's bloodpressure, blood oxygen level, and exercise behavior to a mobilecommunication device of the individual and/or a processing subsystemimplementing portions of the method 100, and Block S120 can includereceiving this data to further augment analyses performed in Block S140.

In relation to receiving data, Blocks S120 and/or S110 can additionallyor alternatively include receiving data pertaining to individuals incontact with the patient during the period of time, such that data fromthe individual who experiences states of psychosis and data from othersin communication with the patient are received (e.g., using informationfrom an analogous application executing on the electronic device(s) ofothers in communication with the individual). As such, Blocks S120and/or S110 can provide a holistic view that aggregates communicationbehavior data and contextual data of two sides of a communicationinvolving the patient who experiences states of psychosis. In examples,such data can include one or more of: a second party's location during aphone call with the patient, the second party's phone number, the secondparty's length of acquaintance with the patient, and the second party'srelationship to the patient (e.g., top contact, spouse, family member,friend, coworker, business associate, etc.).

Similar to Block S110, In relation to receiving the supplementarydataset, Block S120 can include transmitting the supplementary datasetfrom the mobile communication device S122 and/or any other suitabledevice or system that serves as a source of supplementary data, to thecomputing system, as shown in FIG. 2. In transmitting the supplementarydataset, one or more sensor modules (e.g., sensor module of the mobilecommunication device, sensor module of a wearable computing device,sensor of a biometric monitoring device, etc.) can transmit data to thecomputing system by way of a wired and/or wireless data link (e.g., overBluetooth, over Bluetooth LTE, etc.). However, Block S120 can includeany other suitable variation of transmitting supplementary data, and/orreceiving supplementary data.

1.2 Method—Active Data

Block S125 recites: receiving a survey dataset including responses, toat least one of a set of psychosis-assessment surveys, associated with aset of time points of the time period, from the patient. Block S125 thusfunctions to receive active data provided by surveying the patient,which can enable determination of a state of psychosis of the patient.Block S125 is preferably implemented at a module of the computing systemdescribed in relation to Block S110 above, but can additionally oralternatively be implemented at any other suitable system configured toreceive survey data from one or more patients. The survey dataset caninclude interview and/or self-reported information from the patient.Furthermore, the survey dataset preferably includes quantitative data,but can additionally or alternatively include qualitative datapertaining to a psychotic state of the patient corresponding to at leasta subset of the set of time points. Furthermore, while portions of thesurvey dataset preferably correspond to time points within the timeperiod of Block S110, portions of the survey dataset can alternativelycorrespond to time points outside of the time period of Block S110(e.g., as in a pre-screening or a post-screening survey). Additionallyor alternatively, Block S125 can include receiving clinical data (e.g.,information gathered in a clinic or laboratory setting by a clinician).

In Block S125, the set of time points can include uniformly ornon-uniformly-spaced time points, and can be constrained within orextend beyond the time period of the log of use of the nativecommunication application of Block S110. As such, in variations, the setof time points can include regularly-spaced time points (e.g., timepoints spaced apart by an hour, by a day, by a week, by a month, etc.)with a suitable resolution for enabling detection of changes in apsychotic state of the patient. Additionally or alternatively, provisionof a survey and/or reception of responses to a survey can be triggeredupon detection of an event of the patient (e.g., based upon data fromsensors associated with the patient, based upon an output of an analysisof Block S140, etc.) or any other suitable change in psychosis state ofthe patient. Furthermore, for all time points of the set of time points,an identical subset of the set of psychosis-assessment surveys can beprovided to the patient; however, in alternative variations, differentsubsets of the set of psychosis-assessment surveys can be provided tothe patient at different time points of the set of time points.

In variations, the survey dataset can include responses to surveysconfigured to assess severity of psychosis (e.g., along a schizophreniaspectrum) in a patient along a spectrum, wherein the surveys transformqualitative information capturing a patient's affective state intoquantitative data according to a response-scoring algorithm. Inexamples, the set of psychosis-assessment surveys can include surveysderived from one or more of: the Brief Psychiatric Rating Scale (i.e., a16-18 item survey of psychiatric symptom constructs including somaticconcern, anxiety, emotional withdrawal, conceptual disorganization,guilt feelings, tension, mannerisms and posturing, grandiosity,depressive mood, hostility, suspiciousness, hallucinatory behavior,motor retardation, uncooperativeness, unusual thought content, bluntedaffect, excitement, and disorientation, first published in 1962), withscores ranking from 0 (not assessed) to 7 (most severe) for each item; aClinical Global Impression (CGI) rating scale (e.g., A CGI-S severityscale for mental disorders, a CGI-I improvement scale for mentaldisorders, a CGI therapeutic effect scale for mental disorders)configured to assess psychosis symptom severity along a scale of 1(normal) to 7 (most severe), configured to assess psychosis symptomimprovement along a scale of 1 (most improved) to 7 (most severedegradation in state), and/or configured to assess psychosis therapeuticeffect along a 4×4 scale from 1 (unchanged to worse effect) to 2(minimal effect) to 3 (moderate effect) to 4 (effect marked by sideeffects) rated as none to significantly interferes with patient'sfunctioning; a Dimensions of Psychosis Symptom Severity scale providedby the American Psychiatric Association, with scores ranging from 0 (notpresent) to 4 (present and severe) for a set of symptoms (e.g.,hallucinations, delusions, disorganized speech, abnormal psychomotorbehavior, negative symptoms related to emotional expression, impairedcognition, depression, and mania); a Global Functioning Role (GFR)survey for phases of Schizophrenia; a Global Functioning Social (GFS)survey for phases of Schizophrenia; a Community Assessment of PsychicExperiences (CAPE) derived survey; a Scale for the Assessment ofPositive Symptoms (SAPS) derived survey for delusional behavior,hallucinatory behavior, and/or disorganized speech behavior; and anyother suitable tool or survey for assessment of psychosis.

Additionally or alternatively, other survey responses received in BlockS125 can include one or more of: a demographic survey that receivesdemographic information of the patient; a medication adherence survey(for patients taking medication for a psychotic disorder); a moodsurvey; and a social contact survey (e.g., covering questions regardingaspects of the patient's contact with others). However, the set ofsurveys can include any other suitable surveys configured to assessmental states of the patient, or adaptations thereof. As such, thesurvey dataset can include quantitative scores of the patient for one ormore subsets of surveys for each of the set of time points (or a subsetof the set of time points).

In an example, the survey dataset comprises monthly responses to a GFSand a GFR survey, weekly responses (e.g., for a period of 5 months) toquestions derived from the Brief Psychiatric Rating Scale (BPRS) survey,weekly responses (e.g., for a period of 5 months) to questions derivedfrom a CGI survey (e.g., to assess symptoms of depression, anxiety,confusion, suspiciousness, anhedonia, avolition, auditory sensation,visual sensation, sleep, etc.), daily responses to a mood survey (e.g.,a survey that prompts the user to provide an indication of mood on ascale from unhappy to happy), daily responses to a social contact survey(e.g., a survey that asks the user if face-to-face social contactoccurred, a survey that asks the user if any social conflicts occurred),and twice-per-week responses to a medication adherence survey.

In some variations, Block S125 can further include facilitatingautomatic provision of at least one of the set of psychosis-assessmentsurveys at the mobile computing device(s) of the patient(s). As such,responses to one or more of the set of psychosis-assessment surveys canbe provided by user input at an electronic device (e.g., a mobilecomputing device of the patient), or automatically detected from useractivity (e.g., using suitable sensors). Additionally or alternatively,provision of at least one of the set of psychosis-assessment surveys canbe performed manually by an entity associated with a patient or receivedas derived from clinical data, with data generated from the survey(s)received in Block S120 by manual input. Additionally or alternatively,provision of at least one survey and/or reception of responses to thesurvey can be guided by way of an application executing at a device(e.g., mobile device, tablet) of a caretaker of the patient and/or thepatient, wherein the application provides instruction (e.g., in an audioformat, in a graphic format, in a text-based format, etc.) for providingthe survey or the responses to the survey. Block S125 can, however, beimplemented in any other suitable manner (e.g., by verbal communicationover the phone, by verbal communication face-to-face, etc.).

Similar to Block S110, In relation to receiving the survey dataset,Block S125 can include transmitting the survey dataset from the mobilecommunication device S126 and/or any other suitable device or systemthat serves as a source of survey data, to the computing system, asshown in FIG. 2. In transmitting the survey dataset, one or more datastorage modules (e.g., memory module of the mobile communication device,etc.) can transmit data to the computing system by way of a wired and/orwireless data link (e.g., over Bluetooth, over Bluetooth LTE, etc.).However, Block S130 can include another other suitable variation oftransmitting survey data, and/or receiving survey data.

Blocks S110, S120, and S125 can thus provide passive data (e.g.,unobtrusively collected data) and active data (e.g., survey data) thatcan be taken as inputs in Block S130 to generate analyses pertaining topresent, past, and/or future psychotic states of a patient.

Some variations of the method 100 can, however, omit collection of asurvey dataset, such that analyses generated in subsequent blocks of themethod 100 rely upon the log of use dataset and/or the supplementarydataset in enabling determination of a state of a psychosis of thepatient. Thus, analyses of the state(s) of psychosis of the patientand/or predictive models generated in subsequent blocks of the method100 can omit use of active data in determining states of the patient andproviding appropriate therapies to the patient. Alternatively,variations of the method 100 can omit collection of one or more of thelog of use dataset and the supplementary dataset, such that analysesgenerated in subsequent blocks of the method 100 rely upon the surveydataset in enabling determination of a state of a psychosis of thepatient. Thus, analysis of the state(s) of psychosis of the patientand/or predictive models generated in subsequent blocks of the method100 can omit use of portions of the datasets described in Blocks S110,S120, and S125 in determining states of the patient and providingappropriate therapies to the patient. Variations of the method 100 canadditionally or alternatively include omission or collection of anyother suitable type of data for use in subsequent blocks of the method100.

1.3 Method—Modeling and Predicting Psychosis State

Block S130 recites: transforming data derived from at least one the logof use dataset, the supplementary dataset, and the survey dataset intoan analysis of a psychotic episode-risk state associated with at least aportion of the time period. Block S130 functions to determine values ofone or more psychotic episode-risk parameters in association with atleast one time point of the set of time points, based upon one or moreof the log of use dataset, the supplementary dataset, and the surveydataset. Block S130 thus enables assessment of a past or current stateof psychosis of the patient and/or predicts risk that the patient willtrend toward a different (e.g., worsened, improved, etc.) state ofpsychosis at a future time point.

In the analysis, Block S130 can identify parameters/triggering eventsdirectly from passive data (i.e., the log of use dataset, thesupplementary dataset) and/or from active data (i.e., the surveydataset), or can additionally or alternatively implement a predictivemodel that processes either or both passive and active components topredict one or more present or future depressive states of theindividual, with training data. Additionally or alternatively, forpatients following a medication regimen for treatment or maintenance ofhealth in relation to depression, the analyses of Block S130 can includegeneration of an adherence model that assesses or predicts adherence ofthe patient to the medication regimen as an output of the analysis.

1.3.1 Psychotic Episode-Risk State—Predictive Model

Preferably, generating a predictive model S140 in association with BlockS130 includes utilization of one or more machine learning techniques andtraining data (e.g., from the patient, from a population of patients),data mining, and/or statistical approaches to generate more accuratemodels pertaining to the patient's states of psychosis (e.g., over time,with aggregation of more data). As such, Block S130 is preferablyimplemented at a computing system configured to process data from one ormore of the log of use dataset, the supplementary dataset, and thesurvey dataset. The computing system can be the same computing systemassociated with one or more of Blocks S110-S130 of the method 100, orcan alternatively be any other suitable computing system.

In generating the predictive model, Block S140 preferably uses inputdata including one or more of: communication behavior data from the logof use dataset, data from supplementary dataset, and data from thesurvey dataset to provide a set of feature vectors corresponding to timepoints of the time period. Feature selection approaches can include oneor more of: factor analysis approaches that implement statisticalmethods to describe variability among observed features in terms ofunobserved factors, in order to determine which features explain a highpercentage of variation in data; correlation feature selection (CFS)methods, consistency methods, relief methods, information gain methods,symmetrical uncertainty methods, and any other suitable methods offeature selection. In variations, feature selection approaches can beimplemented for any passive data (e.g., communication data,mobility-related data, activity-related data, biometricparameter-related data, etc.), wherein a linking analysis of Block S130is then used to determine associations between features of passive dataand states of a psychosis-related disorder determined from active data(e.g., of the survey dataset). Analysis of the passive data in relationto the active data, with regard to feature selection and associationsbetween passive and active data can, however, be performed in any othersuitable manner.

In one variation, the feature vectors can include features related toaggregate communication behavior, interaction diversity, mobilitybehavior (e.g., mobility radius as a measure of distance traveled by thepatient within a given time period, such as the weekend), a number ofmissed calls, and a duration of time spent in a certain location (e.g.,at home). In examples, feature vectors can be generated based uponaggregation of phone, text message, email, social networking, and/orother patient communication data for a particular period of time intoone or more features for the patient for the particular time period.Furthermore, a feature can be specific to a day, a week, a month, a dayperiod (e.g., morning, afternoon, evening, night), a time block during aday (e.g., one hour), a specific communication action (e.g., a singlephone call, a set of communication actions of the same type (e.g., a setof phone calls within a two-hour period), all communications within aperiod of time, etc.). Additionally, combinations of features can beused in a feature vector. For example, one feature can include aweighted composite of the frequency, duration (i.e., length), timing(i.e., start and/or termination), and contact diversity of all outgoingvoice (e.g., phone call) communications and a frequency, length, andtiming and/or response time to (i.e., time to accept) incoming voicecommunications within the first period of time through a phone callapplication executing on the patient's mobile computing device. Featurevectors can additionally or alternatively include features based onanalysis of voice communications, textual communications, mobileapplication activity usage, location data, and any other suitable datawhich can be based on variance, entropy, or other mathematical andprobabilistic computations of basic data (e.g., a composite activityscore, a composite socialization score, a work-life balance score, aquality-of-life score). However, the feature vectors can be determinedin any other suitable manner.

In some variations, Block S140 can additionally or alternatively includederiving features based upon one or more of: audio data and visual dataof the patient (e.g., during communication, at one or more time pointsof the set of time points), in order to include additional inputs into arisk model for determination of a psychotic-episode-risk parameter. Inone variation, processing of audio data from the patient can be used toaspects of the voice and/or mood of a patient, in order generate featurevectors, incorporating voice-related parameters (e.g., pitch, volume,speed of speech, modulation of speech, differences in voice-relatedparameters for different contacts, etc.) that can be used to detectchanges in the mental state of a patient that are indicative of entranceof a certain psychotic state. Additionally or alternatively, processingof visual data (e.g., images, video) of the patient can be used toaspects of the facial expressions, body language, and/or mood of apatient, in order generate feature vectors, incorporating facialexpression- and/or body language-related parameters (e.g., eyebrowposition, pupil dilation, expressions indicative of positive mood,expressions indicative of negative mood, stance, speed of movement,etc.) that can be used to detect changes in the mental state of apatient that are indicative of entrance of a certain state of psychosis.As such, feature vectors processed by a predictive model in Block S140can include elements derived from audio and/or visual data, in order tocharacterize or anticipate changes in a patient's state of psychosis. Inspecific examples, such features can even be used to classify ordiagnose patients with specific types of psychotic or mental disorders,including disorders along a schizophrenia spectrum, along abipolar-disorder spectrum, and/or any other suitable disorder (e.g., adisorder described in a Diagnostic and Statistical Manual of MentalDisorders).

In some variations, Block S140 can include utilizing statistics-basedfeature selection approaches to determine a subset of features from thelog of use dataset, the supplementary dataset, and/or the survey datasetthat have a high predictive power and/or high accuracy in generating oneor more outputs of the predictive model. In examples, the statisticalapproaches can implement one or more of: correlation-based featureselection (CFS), minimum redundancy maximum relevance (mRMR), Relief-F,symmetrical uncertainty, information gain, decision tree analysis(alternating decision tree analysis, best-first decision tree analysis,decision stump tree analysis, functional tree analysis, C4.5 decisiontree analysis, repeated incremental pruning analysis, logisticalternating decision tree analysis, logistic model tree analysis,nearest neighbor generalized exemplar analysis, association analysis,divide-and-conquer analysis, random tree analysis, decision-regressiontree analysis with reduced error pruning, ripple down rule analysis,classification and regression tree analysis) to reduce questions fromprovided surveys to a subset of effective questions, and otherstatistical methods and statistic fitting techniques to select a subsetof features having high efficacy from the data collected in Blocks S110,S120, and/or S125. Additionally or alternatively, any assessment ofredundancy or efficacy in a feature derived from data of Blocks S110,S120, and/or S125 can be used to provide a measure of confidence in anoutput of the predictive model from one or more input features.Furthermore, the statistical approach(es) of Block S140 can be used tostrategically reduce portions of data collected based upon redundancyand/or lack of utility of the data. Even further, the statisticalapproaches/feature selection approaches can be used to entirely omitcollection of portions of the data (e.g., responses to specific surveysor portions of surveys can render responses to other portions of surveysor other surveys redundant), in order to streamline the data collectionin Blocks S110, S120, and/or S125.

In one example, a high degree of correlation (e.g., positivecorrelation) between responses to a BPRS-derived survey and a daily moodsurvey (e.g., a portion of recent responses to a daily mood survey inrelation to a time point of interest, responses to the daily mood surveyin proximity to responses to a BPRS-derived survey) can be used toentirely omit provision of the BPRS-derived survey or portions of theBPRS-derived survey, in lieu of the daily mood survey, due to redundancyin data collection, in variations of the method 100. In more detail,high degrees of correlation between 1) BPRS positive symptoms andself-reported suspiciousness; 2) BPRS depression/anxiety andself-reported anxiety; 3) BPRS depression/anxiety and self-reportedavolition/amotivation; and 4) BPRS depression/anxiety and self-reportedsadness/depression can be used to omit provision of the BPRS-derivedsurvey or portions of the BPRS-derived survey, and/or portions ofsurveys that prompt self-reported symptoms.

In another example associations 1) BPRS depression/anxiety and amount ofsleep, and/or 2) between BPRS mania and amount of sleep, wherein sleepis self-reported by the patient or determined from a device (e.g.,wearable computing device, bed-coupled device) configured to providesleep data, can be used to omit provision of the BPRS-derived survey orportions of the BPRS-derived survey.

In another example, a high degree of correlation (e.g., positivecorrelation) between responses to a GFR, GFS, and/or CGI-derived surveyand a social contact survey and mobile device usage behavior (e.g.,screen unlock data) can be used to entirely omit provision of the GFR,GFS, and/or CGI-derived survey, in lieu of the social contact survey andmobile usage behavior, due to redundancy in data collection, invariations of the method 100. In still another example, a high degree ofcorrelation (e.g., positive correlation) between a communicationparameter derived from the log of use (e.g., call count predictability)and mobility data from the supplementary dataset can be used to entirelyomit collection of data (e.g., call count data, mobility data) due toredundancy in data collection, in variations of the method 100. In stillanother example, a high degree of correlation (e.g., positivecorrelation) between responses to the mood survey and sleep behavior(e.g., as indicated from usage and/or unlocking of a mobile device) fromthe supplementary dataset can be used to entirely omit collection ofdata (e.g., mood survey responses) due to redundancy in data collection,in variations of the method 100. As such, risk of entering a certainpsychotic state can, with sufficient data and training of a risk model)be determined based solely upon passive data collect from the patient'sinteractions with his/her device(s).

In still other examples, correlations between active data and passivedata can be used to streamline data collection associated with BlocksS110, S120, and/or S125. However, any other suitable data derived fromBlocks S110, S120, and S125 can be used to increase efficacy of datacollection and/or determination of values of the psychotic-episode-riskparameter in Blocks S130 and S140. Additionally or alternatively, anyassessment of redundancy or efficacy in a feature derived from data ofBlocks S110, S120, and/or S125 can be used to provide a measure ofconfidence in a psychotic-episode-risk parameter determined from thefeature(s).

In some embodiments, the predictive model generated in Block S140 canprocess a set of feature vectors according to methods described inrelation to the predictive modeling engine described in U.S. applicationSer. No. 13/969,339, entitled “Method for Modeling Behavior and HealthChanges” and filed on 16 Aug. 2014, which is incorporated herein in itsentirety by this reference; however, the predictive model canalternatively be generated in any other suitable manner. As such, invariations of the model(s), a set of feature vectors from the input datacan be processed according to a machine learning technique (e.g.,support vector machine with a training dataset) to generate the value(s)of the criticality parameter in association with a time point. In oneexample, the predictive model can incorporate historical data from thepatient (e.g., survey responses from a prior week, a history of passivedata from the log of use, etc.), with more weight placed upon morerecent data from Blocks S110-S125 in determination of a psychoticepisode-risk state associated with a time point by the predictive model;however, the predictive model can be implemented in any other suitablemanner.

Furthermore, in extensions of the method 100 to a population ofpatients, the predictive model can be used to identify differences inpassive data and/or active data, as associated with identifieddepression-risk states, between different demographics of individuals.For instance, the predictive model can be used to identify sets offeature vectors and/or subsets of features (e.g., related tocommunication behavior, related to survey responses, related to mobilitybehavior, etc.) that have high efficacy in determining risk/severity forone or more of: different genders, different age groups, differentemployment statuses, different ethnicities, different nationalities,different socioeconomic classes, and any other suitable demographicdifference.

While some variations of machine learning techniques are describedabove, in relation to generation of the predictive model, Block S140 canadditionally or alternatively utilize any other suitable machinelearning algorithms. In variations, the machine learning algorithm(s)can be characterized by a learning style including any one or more of:supervised learning (e.g., using logistic regression, using backpropagation neural networks), unsupervised learning (e.g., using anApriori algorithm, using K-means clustering), semi-supervised learning,reinforcement learning (e.g., using a Q-learning algorithm, usingtemporal difference learning), and any other suitable learning style.Furthermore, the machine learning algorithm can implement any one ormore of: a regression algorithm (e.g., ordinary least squares, logisticregression, stepwise regression, multivariate adaptive regressionsplines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm.

1.3.2 Psychotic Episode-Risk State—Adherence Model

For patients taking medication to manage their depression, Block S140can additionally or alternatively include processing datasets associatedwith Blocks S110, S120, and/or S125 with an adherence model S145configured to assess and/or predict a state of adherence to a medicationregimen by a patient. The adherence model can be an embodiment,variation, or example of an adherence model as described in U.S.application Ser. No. 13/969,339, entitled “Method for Modeling behaviorand Health Changes”, but can alternatively be any other suitableadherence model.

1.3.3 Psychotic Episode-Risk State—Parameters of Analysis andCriticality Assessment

In generating the analysis of a psychotic episode-risk state of theindividual, Block S130 can include generating comparisons betweendifferent threshold conditions and one or more of: components of the logof use dataset, components of the supplementary dataset, components ofthe survey dataset and outputs of the predictive model. As such,generating the analysis of the psychotic episode-risk state of theindividual in Block S130 can include one or more of: generating a firstcomparison between a first threshold condition and a passive datacomponent derived from one or more of the log of use dataset and thesupplementary dataset S131; generating a second comparison between asecond threshold condition and an active data component derived from thesurvey dataset S132; and generating a third comparison between a thirdthreshold condition and an output of the predictive model S133, as shownin FIG. 3. The comparison(s) generated in Blocks S131-S133 can thusindicate that the value(s) of parameters associated with states ofpsychosis (e.g., critical states of psychosis) is/are one or: above theassociated threshold condition(s), not significantly different from theassociated threshold condition(s), and below the associated thresholdcondition(s) in triggering the alert of Block S140, wherein, in aspecific example, alerts are triggered if the value(s) of parametersassociated with a critical state of psychosis is/are above theassociated threshold condition(s). Even further, the comparison(s) tothe threshold condition(s) can be based upon multiple values of aparameter or different parameters (e.g., parameters of active data,parameters of passive data, parameters from a predictive model, etc.) incombination. In Blocks S131-S133, the threshold condition(s) can be athreshold value of a parameter or a threshold range of values, whereinthe threshold range of values is defined by a first limiting value and asecond limiting value. Comparison to the threshold condition(s) inBlocks S131-S133 can thus be performed in a manner that is inclusive ofa limiting value, or alternatively be performed in a manner that isexclusive of a limiting value.

The comparisons of Blocks S131, S132, and/or S133 can thus be associatedwith parameters of the psychotic episode-risk state of the individualused to assess criticality of the state of psychosis of the patient,and/or to resolve a critical state of psychosis of the patient insubsequent blocks of the method 100. Blocks S131, S132, and S133 thusfunction to process the outputs of Blocks S110, S120, and S140 of themethod 100, such that the resolution actions of Block S150 are derivedfrom at least one of an active component (i.e., a component derived fromthe survey response dataset), a passive component (e.g., aclinically-informed behavioral rule component determined by heuristics),and a component derived from the predictive model generated in BlockS142. In particular, consideration of the active component, the passivecomponent, and the component derived from the predictive model cansignificantly strengthen the efficacy of the resolution actionsimplemented in Block S150, as shown in FIG. 4. Furthermore, each of theactive component, the passive component, and the predictive modelcomponent can have an associated time frame that is identical ordifferent to time frames of analysis of the other components.Additionally, analysis of each of the active component, the passivecomponent, and the predictive model component can occur within one ormore time frames that are different from the time frame of an associatedresolution action.

Block S131 recites: generating a first comparison between a firstthreshold condition and a passive data component derived from one ormore of the log of use dataset and the supplementary dataset. In BlockS131, generating the comparison between the first threshold conditionand a passive data element can comprise defining one or more categoriesof passive behaviors of the individual (e.g., related to lethargy,related to social isolation, related to physical isolation, related toevolution of the patient's support network, related to time spent atwork, related to weekly behavioral patterns, etc.) based upon historicalbehavior of a patient within a duration of time (e.g., immediately prior4-6 weeks of the individual's life). Then, Block S131 can includecomparing the features of, or evolution in the passive behavior(s) of,the individual to the first threshold condition. In variations whereinthe passive behaviors of the patient are monitored for a duration oftime, the first threshold condition can additionally or alternativelyinclude a frequency threshold and/or afrequency-within-a-duration-of-time threshold, in relation to generationof an indication based upon a passive data component.

In variations, the first threshold condition can include one or more of:a threshold condition of a mobility-related parameter than the 10^(th)percentile of values of the mobility-related parameter (e.g., mobilityradius) for the time period (e.g., a time window of 30 days, including15 values of the mobility-related parameter) for the patient; athreshold condition of a mobility less than the 20^(th) percentile ofvalues of a mobility-related parameter (e.g., mobility radius) for thetime period (e.g., a time window of 30 days, including 15 values of themobility-related parameter); a threshold condition of a number ofoutgoing SMS messages less than 2 messages per day for a period of threeconsecutive days (e.g., which is correlated with higher BPRS positivesymptom scores and higher BPRS depressive symptom scores); a thresholdcondition of a number of incoming SMS messages less than 2 messages perday for a period of 5 consecutive days; any other suitable thresholdcondition; and any other suitable combination of threshold conditions.

In examples, the first comparison can thus facilitate identification ofone or more of: a period of social isolation exhibited as persistence inreduced outgoing communications and/or reduced incoming communications(e.g., a period of 2 days of unreturned phone calls, a period of 2 daysof unreturned text-based communications, etc.); a period of physicalisolation exhibited as persistence in staying in a location (e.g.,staying primarily at the same location for a period of 3 or more days);a reduction in the individual's support network exhibited ascommunicating with fewer people than typical for the patient; a periodof lethargy exhibited as a persistent reduction in mobility (e.g.,little motion over a period of 3 consecutive days); a combination ofmultiple passive behaviors that satisfy a threshold condition (e.g., twopassive behaviors that meet a threshold within 3 days); and any othersuitable condition for indication generation.

Block S132 recites: generating a second comparison between a secondthreshold condition and an active data component derived from the surveydataset. In Block S132, generating the second comparison between thesecond threshold condition and the active component derived from thesurvey response dataset can comprise assigning a score to one or moreelements of the survey response dataset for a patient (e.g., based uponone instance of survey response provision, based upon multiple instancesof survey response provision), and comparing the score(s) to the secondthreshold condition. In variations wherein the survey response datasetcomprises responses to survey questions (e.g., a repeat set of surveyquestions) at each of a set of time points, the second thresholdcondition can additionally or alternatively include a frequencythreshold and/or a frequency-within-a-duration-of-time threshold, inrelation to generation of an indication based upon an active component.Furthermore, threshold conditions can be defined in relation to abaseline for each patient, as determined from historical behavior of thepatient.

As such, in variations, the second comparison can indicate one or moreof: a score greater than a given threshold; a score greater than a giventhreshold for a certain duration of time; a change in score greater thana given threshold; a change in score greater than a given threshold asderived from the patient's historical score data; and any other suitablecomparison. Furthermore, the comparison(s) can additionally oralternatively be generated based upon a percentile condition, a standarddeviation (e.g., in score) condition, outlier detection analysis (e.g.,of a score in relation to scores from the individual), and/or any othersuitable condition, based upon analysis of a patient in isolation, basedupon analysis of the individual's recent behavior in isolation, basedupon analysis of a population of patients, and/or any other suitablegrouping of patients.

In examples, the second comparison can comprise a comparison to one ormore of: a threshold condition of three (e.g., consecutive,non-consecutive) BPRS derived survey scores greater than or equal to 70,with a difference greater than 20; a threshold condition of one BPRSsurvey score greater than 90; a threshold condition of a score greaterthan 0 on a portion of a psychosis-assessment survey related to self oroutwardly afflicted harm; a threshold condition of scores fluctuatingsignificantly across three consecutive days on a mood survey; athreshold condition pertaining to a period of time (e.g., 1 day, 3 days)for a lapse in adherence to a medication regimen; and a thresholdcondition of scores fluctuating significantly across five consecutivedays on a social contact survey (e.g., in relation to face-to-facesocial contact, in relation to number of conflicts).

Block S133 recites: generating a third comparison between a thirdthreshold condition and an output of the predictive model. In BlockS133, generating the third comparison between the third thresholdcondition and the output of the predictive model can compriseidentification of a classification (e.g., a learned, complex,non-intuitive, and/or behavioral association exhibited by theindividual), and comparing the classification to a threshold condition.In variations, a single feature and/or combinations of features derivedfrom the log of use dataset, the supplementary dataset, and, the surveyresponse dataset (e.g., with weighting among factors) can be compared toone or more threshold conditions, in identifying if an alert based uponthe predictive model of Block S140 should be generated. In variationsand examples, the third comparison can be generated as described in U.S.application Ser. No. 13/969,339, entitled “Method for Modeling Behaviorand Health Changes” and filed on 16 Aug. 2014.

As such, in one example of Blocks S131, S132, and S133, accounting for apassive component, an active component, and a predictive modelcomponent, a determination of a state of psychosis of the patient can bebased upon: a first passive component (e.g., related to communicationbehavior) generated from behavior analyzed over a first duration oftime, a second passive behavioral component (e.g., related to mobilityof the individual) generated from behavior analyzed over a secondduration of time overlapping with the first duration of time, scoring ofa biweekly survey, and a predictive model component for third durationof time (e.g., overlapping with the period of the biweekly survey),wherein the predictive model component implements an aggregated learningapproach based upon multiple individual models (e.g., each assessingdifferent parameters and/or different time periods of patient behavior).

The analyses of Block S130 can, however, include generation of any othersuitable comparison and/or any other suitable output which serve asparameters of the psychotic episode-risk state of the individual.Additionally or alternatively, the comparison(s) generated in BlocksS131, S132, and S133 can include identification or analysis of patientprogress through a condition (e.g., in relation to persistence ofsymptoms, in relation to worsening of symptoms, in relation toimprovement of symptoms, etc.).

1.4 Method—Resolution of Critical States of Psychosis

Block S150 recites: generating an alert based upon one or more outputsof the analysis, which functions to provide an indication that thepatient is experiencing a critical state of psychosis and/or is trendingtoward a critical state of psychosis. Block S150 can thus includegenerating an alert upon detection, at the computing system performingthe analysis, that one or more outputs (e.g., comparisons) from theanalysis of the psychotic episode-risk state satisfy associatedthreshold conditions. The alert of Block S150 can be an alert thatprompts transmission of a notification to an entity associated with thepatient, for instance, for therapeutic intervention. The alert canadditionally or alternatively comprise an alert that serves as an inputinto a subsequent computer-implemented module for automaticallyproviding an intervention to the patient, the intervention intended toimprove the psychosis-related state of the patient.

As such, Block S150 can include Block S152, which recites: transmittingan alert based upon the analysis. Block S152 functions to alert at leastone of: 1) an entity associated with the patient and 2) the patientregarding a critical value of one or more values of thepsychotic-episode-risk parameters, or of a critical state of psychosisthat the patient has or will enter. The alert can be a visual alert(e.g., text-based alert, graphic alert), audio alert, haptic alert,and/or any other suitable type of alert. In relation to an entityassociated with the patient(s), the entity can include any one or moreof: a caretaker, a healthcare provider, a relative (e.g., parent,significant other, etc.), and any other suitable entity associated withthe patient. Furthermore, in relation to an entity associated with thepatient(s), the alert(s) can be provided at a dashboard of an electronicinterface (e.g., web portal, computing device, etc.) accessible by theentity. In the example shown in FIG. 4, alert(s) of Block S152 can beprovided at a dashboard of a web portal, wherein the alert(s) aretext-based alerts including a type of alert (e.g., related to activedata, related to passive data), a value of a psychotic-episode-riskparameter associated with the alert, and a graphic that displays valuesof one or more scores of a survey (e.g., a daily mood survey) and/or apsychotic-episode-risk parameter over time. In the example, the graphiccan include tags that facilitate identification of associations betweenmetrics derived from active data and passive data (e.g., mobilityparameter values in association with scores on a BPRS, GFR, GFS, or CGIderived survey, scores on a social contact survey, and/or scores on adaily mood survey). The dashboard can further provide an option toresolve the alert, wherein in examples, resolution of the alert caninclude any one or more of: triaging a patient's psychotic state,providing emotional support to the patient to improve the patient'spsychotic state, assessing the level of follow up care needed to improvethe patient's state (e.g., by facilitating an appointment with a primarycare physician within 3 days, by alerting a friend of the patient, byfacilitating immediate transfer of the patient to an emergency room,etc.), by providing a short term plan to the patient to improve thepatient's psychotic state in an acute manner, by providing a long termplan to the patient that is configured to maintain a healthy state ofthe patient, and any other suitable resolving act (e.g., storinginformation/data resulting from a resolution action for futurereference).

In relation to the comparison(s) of Blocks S131, S132, and S133, thealert can comprise an alert associated with passive data (e.g., alertsrelated to lethargy associated with a mobility parameter, alerts relatedto social isolation in association with unreturned calls, alerts relatedto physical isolation in association with time spent at a locationalone, alerts associated with reaching out to a support networkassociated with number of communication counts, alerts associated withreaching out to a support network associated with communicationdiversity, etc.). Additionally or alternatively, the alert can comprisean alert associated with active data (e.g., alerts related to high BPRSscores, alerts related to alerts related to fluctuations in daily moodscores, alerts related to medication adherence, alerts related to poorsocial contact, etc.). However, in variations of the specific examplesnoted above, the alerts can be associated with any other suitable formof active/passive data derived from other blocks of the method 100. Assuch, the alert can comprise any other suitable alert configured tofacilitate improvement of the psychotic state of the patient, includingidentification of factors or triggers (e.g., substance abuse, medicationnon-adherence, stressful life events, natural course of psychoticillness, etc.) contributing to lapses in the patient's psychotic state,in order to intervene at an acute phase of the patient's psychosis.

In some variations, the method 100 can further include Block S160, whichrecites: providing a notification to the patient, at the mobilecommunication device, in response to the analysis. Block S160 functionsto provide information, advice, and/or motivational content to thepatient so that the patient can improve his/her psychotic state, canavoid conflicts, can avoid situations that could worsen his/herpsychotic state, and/or can maintain a healthy state. In variations ofBlock S160, the notifications can be provided with any suitable regularor non-regular frequency, can be provided with a sequence or in a randommanner, can be triggered by an event, or can be provided in any othersuitable manner. Furthermore, the notifications can include one or moreof: a visual notification (e.g., text-based notification, graphicnotification), an audio notification, a haptic notification, and anyother suitable type of notification. In one example, a mobile computingdevice of a patient can download and subsequently display thenotification for the patient at a display of the mobile computingdevice, as shown in FIGS. 5A-5C, where examples of notifications aredepicted as S160′, S160″, and S160″′. The notifications can bepersonalized to the patient, or can be provided in the same manner toeach of a population of patients. In variations wherein thenotifications are personalized to the patient, Block S160 can utilize amachine learning technique or any other suitable computational process,as described above, to identify the types of notifications that thepatient responds positively to and/or negatively to, as assessed bypatient outcomes in relation to psychotic state (e.g., indicated invalues of the psychotic-episode-risk parameter).

In one example, the notification can provide advice to the patient,based upon the analysis, to avoid contact with people identified inBlocks S130 and S140 to produce degradation in the patient's psychoticstate, based upon the analysis. In another example, the notification canprovide advice to the patient, based upon the analysis, to avoid travelto certain locations identified in Blocks S130 and S140 to producedegradation in the patient's state of psychosis. In another example, thenotification can notify the user of a period of lack of social contact,and can additionally or alternatively set reminders for the patient tocontact entities identified to have a positive impact on the patient'sstate of psychosis. In another example, the notification can provide theuser with incentives (e.g., coupons, discounts) to increase his/hermobility or social contact, thereby preventing degradation in thepatient's state of psychosis. The notification can additionally oralternatively be provided as described in U.S. application Ser. No.13/969,339, entitled “Method for Modeling Behavior and Health Changes”,and/or in any other suitable manner.

In some variations, the method 100 can additionally or alternativelyinclude Block S170, which recites: automatically initiating provision ofa therapeutic intervention for the individual by way of at least one ofthe computing system and the mobile communication device. Block S170functions to actively promote improvements to the patient's psychoticstate, and/or to facilitate maintenance of a healthy state in thepatient. In variations, the therapeutic intervention can include atherapy regimen that delivers therapeutic measures to the patient, asfacilitated by at least one of the computing system and the mobilecommunication device. Generation and/or provision of the therapeuticintervention(s) can thus be facilitated through one or more of: anapplication executing on an electronic device (e.g., mobile device,tablet, personal computer, head-mounted wearable computing device,wrist-mounted wearable computing device, etc.) of the patient, a webapplication accessible through an internet browser, an entity (e.g.,caretaker, spouse, healthcare provider, relative, acquaintance, etc.)trained to provide the therapy regimen, and in any other suitablemanner. In examples, portions of a therapy regimen can be deliveredin-app through the mobile communication device, and/or interactionsbetween the patient and a therapeutic entity can be established usingmodules of the computing system.

In variations, the therapeutic measures provided in Block S170 caninclude any one or more of: psychiatric management measures (e.g.,education of the patient, education of acquaintances of the patient,forming alliances, providing support groups, etc.), pharmacotherapeuticmeasures (e.g., antipsychotic medications, benzodiazepines,antidepressants, mood stabilizers, beta blockers), psychotherapeuticmeasures (e.g., cognitive behavioral therapy, interpersonal therapy,problem solving therapy, psychodynamic psychotherapy), psychosocialinterventions, weight management interventions (e.g., to prevent adverseweight-related side effects due to medications) electroconvulsivetherapeutic measures, and any other suitable therapeutic measure.

Furthermore, the therapy regimen and/or other therapeutic interventionscan be provided using one or more of: healthcare provider interactions(e.g., therapeutic sessions with a counselor), pharmaceutical compounddistributors, mobile application implemented methods, webbrowser-facilitated methods, and any other suitable avenue of therapyprovision. The therapeutic interventions of Block S170 can additionallyor alternatively be provided in a manner similar to that described inU.S. application Ser. No. 13/969,339, entitled “Method for ModelingBehavior and Health Changes”, with therapy/treatment efficacy analyzedby a treatment regimen model and/or a treatment efficacy model. Thetherapy regimen can, however, be provided in any other suitable manneror assessed in any other suitable manner.

The method 100 can, however, include any other suitable blocks or stepsconfigured to model behavior and psychotic state, and/or improve apsychotic state of a patient. Furthermore, as a person skilled in theart will recognize from the previous detailed description and from thefigures and claims, modifications and changes can be made to the method100 without departing from the scope of the method 100.

2. System

As shown in FIG. 6, a system 200 for modeling behavior and psychosis ofa patient includes: a processing system 205 including: an interface 207with a native data collection application executing on a mobilecomputing device 209 of the patient; a first module 210 configured toaccess a log of use of a native communication application coupled to thenative data collection application on the mobile computing device by thepatient within a time period; a second module 220 configured to receivea supplementary dataset characterizing activity of the patient inassociation with the time period; a third module 230 configured toreceive a survey dataset including responses, to at least one of a setof psychosis-assessment surveys, associated with a set of time points ofthe time period, from the patient; a fourth module 240 configured totransform data from the log of use, the survey dataset, and thesupplementary dataset into an analysis of a psychotic episode-risk stateof the individual; and a fifth module 250 configured to generate analert based upon one or more outputs of the analysis. The system 200 canincorporate, at least in part, embodiments, variations, and examples ofelements of the system described in U.S. application Ser. No. 13/969,339entitled “Method for Modeling Behavior and Health Changes” and filed on16 Aug. 2013.

The system 200 functions to perform at least a portion of the method 100described in Section 1 above, but can additionally or alternatively beconfigured to perform any other suitable method for modeling behaviorand psychosis of a patient. The system 200 is preferably configured tofacilitate reception and processing of a combination of active data(e.g., survey responses) and passive data (e.g., unobtrusively collectedcommunication behavior data, mobility data, etc.), but can additionallyor alternatively be configured to receive and/or process any othersuitable type of data. As such, the processing system 205 can beimplemented on one or more computing systems including one or more of: acloud-based computing system (e.g., Amazon EC3), a mainframe computingsystem, a grid-computing system, and any other suitable computingsystem. Furthermore, reception of data by the processing system 205 canoccur over a wired connection and/or wirelessly (e.g., over theInternet, directly from a natively application executing on anelectronic device of the patient, indirectly from a remote databasereceiving data from a device of the patient, etc.).

The processing system 205 and data handling by the modules of theprocessing system 205 are preferably adherent to health-related privacylaws (e.g., HIPAA), and are preferably configured to privatize and/or oranonymize patient data according to encryption protocols. In an example,when a patient installs and/or authorizes collection and transmission ofpersonal communication data by the system 200 through the native datacollection application, the native application can prompt the patient tocreate a profile or account. In the example, the account can be storedlocally on the patient's mobile computing device 209 and/or remotely.Furthermore, data processed or produced by modules of the system 200 canbe configured to facilitate storage of data locally (e.g., on thepatent's mobile computing device, in a remote database), or in any othersuitable manner. For example, private health-related patient data can bestored temporarily on the patient's mobile computing device in a lockedand encrypted file folder on integrated or removable memory. In thisexample, the patient's data can be encrypted and uploaded to the remotedatabase once a secure Internet connection is established. However,patient data can be stored on any other local device or remote data inany other suitable way and transmitted between the two over any otherconnection via any other suitable communication and/or encryptionprotocol. As such, the modules of the system 200 can be configured toperform embodiments, variations, and examples of the method 100described above, in a manner that adheres to privacy-related healthregulations.

The method 100 and/or system 200 of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated by computer-executablecomponents integrated with apparatuses and networks of the typedescribed above. The computer-readable medium can be stored on anysuitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component can be a processor,though any suitable dedicated hardware device can (alternatively oradditionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

We claim:
 1. A method for improving psychotic disorder-related statedetermination for a patient, the method comprising: transmitting, from acommunication module executing on a mobile communication device to acomputing system, a log of use dataset associated with communicationbehavior of the patient during a time period; at the computing system,receiving a motion supplementary dataset corresponding to a motionsensor of the mobile computing device, the motion supplementary datasetcharacterizing mobility-behavior of the patient during the time period;collecting GPS data corresponding to a GPS sensor of the mobile device,the GPS data associated with location behavior of the patient inassociation with the time period; selecting a patient subgroup for thepatient from a first subgroup and a second subgroup based on the GPSdata and the motion supplementary dataset, wherein the first subgroup isselected in response to the GPS data and the motion supplementarydataset indicating a first mobility behavior associated with the firstsubgroup, wherein the second subgroup is selected in response to the GPSdata and the motion supplementary dataset indicating a second mobilitybehavior associated with the second subgroup, and wherein selection ofthe patient subgroup is operable to improve data storage, dataretrieval, and the psychotic disorder-related state determination; atthe computing system, generating a predictive model based upon theselected patient subgroup and a passive dataset derived from the log ofuse dataset and the motion supplementary dataset; transforming at leastone of the passive dataset and an output of the predictive model into ananalysis of a psychotic episode-risk state of the patient associatedwith at least a portion of the time period; and upon detection thatparameters of the psychotic episode-risk state satisfy at least onethreshold condition, automatically initiating provision of a therapeuticintervention for improving a health outcome of the patient, by way of atleast one of the computing system and the mobile communication device.2. The method of claim 1, further including: providing, by way of anapplication of the mobile communication device, at least one of a set ofpsychosis-assessment surveys to the patient; and at the computingsystem, receiving a survey dataset including responses, to at least oneof the set of psychosis-assessment surveys, associated with a set oftime points of the time period.
 3. The method of claim 2, whereinproviding at least one of a set of psychosis-assessment surveys includesautomatically providing a first portion of a survey within theapplication at a first subset of time points of the time period, andproviding a second portion of the survey within the application at asecond subset of time points of the time period.
 4. The method of claim2, wherein the survey dataset includes responses to a Brief PsychiatricRating Scale (BPRS)-derived survey, a mood information survey, and asleep information survey, and wherein generating the predictive modelcomprises generating the predictive model from a) the survey dataset andb) the passive dataset.
 5. The method of claim 4, wherein generating theanalysis includes determining an amount of sleep per night of thepatient over a portion of the time period from the sleep informationsurvey, and generating an alert configured to initiate provision of thetherapeutic intervention, upon detection that the amount of sleep isbelow a threshold amount and that responses to the BPRS-derived surveycontribute to a BPRS score above a threshold score.
 6. The method ofclaim 1, wherein transmitting the log of use dataset further includes:at the computing system, extracting, from the log of use dataset: anumber of incoming text messages and a number of outgoing text messages.7. The method of claim 6, wherein receiving the motion supplementarydataset includes extracting at least one of a location of the patientand mobility-related parameter associated with at least one time pointof the time period and derived from the GPS sensor of the mobilecommunication device.
 8. The method of claim 7, wherein generating theanalysis includes generating a first comparison between a) a firstthreshold condition, associated with reduced communication behavior ofthe patient across a portion of the time period, and b) at least onepassive data element of the log of use dataset and the supplementarydataset.
 9. The method of claim 1, wherein generating the predictivemodel includes: at the computing system, implementing a factor analysisapproach that models changes in text messaging behavior of the patientacross at least a portion of the time period.
 10. The method of claim 1,wherein automatically initiating provision of the therapeuticintervention includes enabling a healthcare entity to contact thepatient within an application executing at the mobile computing device,thereby preventing the patient from trending toward psychoticepisode-risk state.
 11. A method for improving psychoticdisorder-related state determination for a patient, the methodcomprising: transmitting, from a communication module executing on amobile communication device to a computing system, a log of use datasetassociated with communication behavior of the patient during a timeperiod; at the computing system, receiving a mobility sensorsupplementary dataset corresponding to a mobility sensor of the mobilecommunication device, the mobility sensor supplementary datasetcharacterizing physical activity of the patient during the time period;selecting a patient subgroup for the patient from a first subgroup and asecond subgroup based on the mobility sensor supplementary dataset,wherein the first subgroup is selected in response to the mobilitysensor supplementary dataset indicating a first mobility behaviorassociated with the first subgroup, wherein the second subgroup isselected in response to the mobility sensor supplementary datasetindicating a second mobility behavior associated with the secondsubgroup, and wherein selection of the patient subgroup is operable toimprove data storage, data retrieval, and the psychotic disorder-relatedstate determination; at the computing system, generating a predictivemodel of a psychotic episode-risk state of the patient associated withat least a portion of the time period, based on the selected patientsubgroup and at least one of the log of use dataset and thesupplementary dataset; and by way of at least one of the computingsystem and the mobile communication device, initiating provision of atherapeutic intervention for improving a health outcome of the patient,upon detection that a set of parameters outputted from the predictivemodel of the psychotic episode-risk state satisfy at least one thresholdcondition.
 12. The method of claim 11, further comprising: providing, byway of an application of the mobile communication device, at least oneof a set of psychosis-assessment surveys to the patient; and at thecomputing system, receiving a survey dataset including responses, to atleast one of the set of psychosis-assessment surveys, associated with aset of time points of the time period.
 13. The method of claim 12,wherein the survey dataset includes responses to a Brief PsychiatricRating Scale (BPRS)-derived survey and a sleep information survey, andwherein generating the predictive model comprises generating thepredictive model from a) the survey dataset and b) a passive datasetincluding the log of use dataset and the supplementary dataset.
 14. Themethod of claim 11, wherein transmitting the log of use dataset furtherincludes: at the computing system, extracting, from the log of usedataset: a number of incoming text messages and a number of outgoingtext messages.
 15. The method of claim 14, wherein initiating provisionof the therapeutic intervention comprises initiating provision of thetherapeutic intervention upon detection that at least one of: a) thenumber of incoming text messages and b) the number of outgoing textmessages is below a threshold number of text messages.
 16. The method ofclaim 11, wherein generating the predictive model includes: at thecomputing system, implementing an analytical approach that modelschanges in text messaging behavior of the patient across at least aportion of the time period.
 17. The method of claim 16, whereingenerating the predictive model includes processing the log of usedataset and the supplementary dataset with a correlation-based featureselection (CFS) algorithm to extract features of the psychoticepisode-risk state across the time period.
 18. The method of claim 11,wherein generating the predictive model of the psychotic episode-riskstate of the patient includes generating an anticipatedpsychosis-related state of the user at a future time point outside ofthe time period, and wherein the method further includes automaticallyinitiating provision of a therapeutic intervention for the patient toprevent the anticipated psychosis-related state of the patient.
 19. Themethod of claim 18, wherein the anticipated psychosis-related state is aschizophrenic state, and wherein automatically initiating provision ofthe therapeutic intervention includes enabling a healthcare entity tocontact the patient proximal in time to the future time point.
 20. Themethod of claim 11, wherein initiating provision of the therapeuticintervention comprises rendering the alert at a user interface of adashboard of a healthcare entity associated with the patient, andwherein rendering the alert further includes providing the healthcareentity with an electronically-implemented tool for contacting thepatient.